Agentic Architecture & Orchestration

Knowledge of

• The agentic loop lifecycle: sending requests to Claude, inspecting stop_reason ("tool_use" vs "end_turn"), executing requested tools, and returning results for the next iteration
• How tool results are appended to conversation history so the model can reason about the next action
• The distinction between model-driven decision-making (Claude reasons about which tool to call next based on context) and pre-configured decision trees or tool sequences

Skills in

• Implementing agentic loop control flow that continues when stop_reason is "tool_use" and terminates when stop_reason is "end_turn"
• Adding tool results to conversation context between iterations so the model can incorporate new information into its reasoning
• Avoiding anti-patterns such as parsing natural language signals to determine loop termination, setting arbitrary iteration caps as the primary stopping mechanism, or checking for assistant text content as a completion indicator

Knowledge of

• Hub-and-spoke architecture where a coordinator agent manages all inter-subagent communication, error handling, and information routing
• How subagents operate with isolated context -- they do not inherit the coordinator's conversation history automatically
• The role of the coordinator in task decomposition, delegation, result aggregation, and deciding which subagents to invoke based on query complexity
• Risks of overly narrow task decomposition by the coordinator, leading to incomplete coverage of broad research topics

Skills in

• Designing coordinator agents that analyze query requirements and dynamically select which subagents to invoke rather than always routing through the full pipeline
• Partitioning research scope across subagents to minimize duplication (e.g., assigning distinct subtopics or source types to each agent)
• Implementing iterative refinement loops where the coordinator evaluates synthesis output for gaps, re-delegates to search and analysis subagents with targeted queries, and re-invokes synthesis until coverage is sufficient
• Routing all subagent communication through the coordinator for observability, consistent error handling, and controlled information flow

Knowledge of

• The Task tool as the mechanism for spawning subagents, and the requirement that allowedTools must include "Task" for a coordinator to invoke subagents
• That subagent context must be explicitly provided in the prompt -- subagents do not automatically inherit parent context or share memory between invocations
• The AgentDefinition configuration including descriptions, system prompts, and tool restrictions for each subagent type
• Fork-based session management for exploring divergent approaches from a shared analysis baseline

Skills in

• Including complete findings from prior agents directly in the subagent's prompt (e.g., passing web search results and document analysis outputs to the synthesis subagent)
• Using structured data formats to separate content from metadata (source URLs, document names, page numbers) when passing context between agents to preserve attribution
• Spawning parallel subagents by emitting multiple Task tool calls in a single coordinator response rather than across separate turns
• Designing coordinator prompts that specify research goals and quality criteria rather than step-by-step procedural instructions, to enable subagent adaptability

Knowledge of

• The difference between programmatic enforcement (hooks, prerequisite gates) and prompt-based guidance for workflow ordering
• When deterministic compliance is required (e.g., identity verification before financial operations), prompt instructions alone have a non-zero failure rate
• Structured handoff protocols for mid-process escalation that include customer details, root cause analysis, and recommended actions

Skills in

• Implementing programmatic prerequisites that block downstream tool calls until prerequisite steps have completed (e.g., blocking process_refund until get_customer has returned a verified customer ID)
• Decomposing multi-concern customer requests into distinct items, then investigating each in parallel using shared context before synthesizing a unified resolution
• Compiling structured handoff summaries (customer ID, root cause, refund amount, recommended action) when escalating to human agents who lack access to the conversation transcript

Knowledge of

• Hook patterns (e.g., PostToolUse) that intercept tool results for transformation before the model processes them
• Hook patterns that intercept outgoing tool calls to enforce compliance rules (e.g., blocking refunds above a threshold)
• The distinction between using hooks for deterministic guarantees versus relying on prompt instructions for probabilistic compliance

Skills in

• Implementing PostToolUse hooks to normalize heterogeneous data formats (Unix timestamps, ISO 8601, numeric status codes) from different MCP tools before the agent processes them
• Implementing tool call interception hooks that block policy-violating actions (e.g., refunds exceeding $500) and redirect to alternative workflows (e.g., human escalation)
• Choosing hooks over prompt-based enforcement when business rules require guaranteed compliance

Knowledge of

• When to use fixed sequential pipelines (prompt chaining) versus dynamic adaptive decomposition based on intermediate findings
• Prompt chaining patterns that break reviews into sequential steps (e.g., analyze each file individually, then run a cross-file integration pass)
• The value of adaptive investigation plans that generate subtasks based on what is discovered at each step

Skills in

• Selecting task decomposition patterns appropriate to the workflow: prompt chaining for predictable multi-aspect reviews, dynamic decomposition for open-ended investigation tasks
• Splitting large code reviews into per-file local analysis passes plus a separate cross-file integration pass to avoid attention dilution
• Decomposing open-ended tasks (e.g., "add comprehensive tests to a legacy codebase") by first mapping structure, identifying high-impact areas, then creating a prioritized plan that adapts as dependencies are discovered

Knowledge of

• Named session resumption using --resume <session-name> to continue a specific prior conversation
• fork_session for creating independent branches from a shared analysis baseline to explore divergent approaches
• The importance of informing the agent about changes to previously analyzed files when resuming sessions after code modifications
• Why starting a new session with a structured summary is more reliable than resuming with stale tool results

Skills in

• Using --resume with session names to continue named investigation sessions across work sessions
• Using fork_session to create parallel exploration branches (e.g., comparing two testing strategies or refactoring approaches from a shared codebase analysis)
• Choosing between session resumption (when prior context is mostly valid) and starting fresh with injected summaries (when prior tool results are stale)
• Informing a resumed session about specific file changes for targeted re-analysis rather than requiring full re-exploration